Project Summary Malaria that results from Plasmodium falciparum is among the most globally devastating human diseases. The principle vector of malaria, mosquitoes of the Anopheles gambiae species complex, are thus central targets for controlling the human health burden of Plasmodium. For nearly two decades, there have been large-scale, coordinated efforts to diminish mosquito populations, generally through spraying and insecticide treated bed nets. Indeed such control efforts have now led to a nearly 50% decrease in the rates of malaria infection in many parts of sub-Saharan Africa. At present, however, control efforts of A. gambiae are being threatened by evolutionary responses within mosquitos: A. gambiae populations have shown increases in insecticide resistance as well as behavioral adaptations that allow mosquitos to avoid spraying all together. Thus adaptation of mosquitos to the control efforts themselves is currently a risk to maintain the gains made in the fight against malaria. In this proposal we lay out an integrated population genomic approach for systematically identifying regions of the A. gambiae genome that are evolving adaptively in response to ongoing control efforts. Our approach centers upon state-of-the-art supervised machine learning techniques that we have recently introduced for finding the signatures of selective sweeps in genomes (Schrider and Kern, 2016), coupled with the large-scale population genomic datasets currently in production by the Ag1000G consortium.